Generalizable long short-term memory models for beef cattle DMI under grazing

Estimating individual dry matter intake (DMI) on pasture remains costly, labor-intensive, and episodic. We developed a deployable sequence model to predict daily, per-animal DMI using walk-on scale weights, metered water intake, animal metadata, and publicly available weather. A 3-layer long short-term memory (LSTM) network (hidden size = 256, dropout = 0.20) ingested 7-d windows of engineered covariates, including rolling statistics, first- and second-order differences, short lags, thermal ranges, and temperature-humidity index (THI), scaled with a RobustScaler; categorical variables were encoded with learned embeddings. The prediction head was a compact multilayer perceptron, and outputs were inverse Box-Cox transformed to kilograms per day. Training used animal-identity-grouped splits, Huber loss, Adam optimization with gradient clipping, learning-rate scheduling, early stopping, and Optuna hyperparameter tuning. A single feature-scaler bundle was evaluated against National Academies of Sciences, Engineering, and Medicine (NASEM) equations and three strictly held-out external validations: regional drylot (Morgantown, WV), non-regional drylot (Hyplains, KS), and grazing (2023 Wardensville, WV). Using identical evaluation masks, the LSTM improved accuracy relative to NASEM (pooled RMSE 1.329 vs. 1.858 kg/d; R2 0.655 vs. 0.326), with the largest gains in grazing (LSTM RMSE 1.180, R2 0.507; NASEM RMSE 3.883, R2 -4.337). Iterative fine-tuning with a frozen encoder showed stable behavior under bias-only head updates across production systems. These results demonstrate that accurate, generalizable DMI prediction can be achieved from pragmatic inputs, enabling scalable evaluation of intake phenotypes and feed-efficiency traits directly in grazing systems.